Imitation Learning: Imitation learning is a supervised learning problem where an agent extracts a policy from a dataset of demonstrations. The two most common approaches to imitation are Behavior Cloning [1]}, [2]} and Inverse Reinforcement Learning (IRL) [3]}. BC approaches learn policies \(\pi _\theta (a|s)\)  that most closely match the state-conditioned action distribution of the demonstration data. IRL approaches learn a reward function from the demonstration data assuming that the demonstrations are near-optimal for a desired task and utilize Reinforcement Learning to produce policies that maximize the reward. For simplicity and to avoid learning a reward function, in this work we aim to learn generalizable skills and using the BC approach. However, two drawbacks of BC are that the imitation policies require a large number of demonstrations and are prone to drifting away from the demonstration distribution during evaluation due to error accumulation [2]}. For this reason, BC policies work best when the time-horizon of the task is short.
